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Record W2791869041 · doi:10.1016/j.ekir.2018.02.002

Remote Dwelling Location Is a Risk Factor for CKD Among Indigenous Canadians

2018· article· en· W2791869041 on OpenAlexaffabout
Oksana Harasemiw, Shannon Milks, Louise Oakley, Barry Lavallee, Caroline Chartrand, Lorraine McLeod, Michelle Di Nella, Claudio Rigatto, Navdeep Tangri, Thomas W. Ferguson, Paul Komenda

Bibliographic record

VenueKidney International Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity of ManitobaFirst Nations Health and Social Secretariat of ManitobaSeven Oaks General Hospital
Fundersnot available
KeywordsIndigenousMedicineKidney diseaseDiabetes mellitusPovertyPopulationRural areaEnvironmental healthGerontologyPublic healthDemographyInternal medicineEcologyPathologyEndocrinology

Abstract

fetched live from OpenAlex

INTRODUCTION: Rural and remote indigenous individuals have a high burden of chronic kidney disease (CKD) when compared to the general population. However, it has not been previously explored how these rates compare to urban-dwelling indigenous populations. METHODS: In a recent cross-sectional screening study, 1346 adults 18 to 80 years of age were screened for CKD and diabetes across 11 communities in rural and remote areas in Manitoba, Canada, as part of the First Nations Community Based Screening to Improve Kidney Health and Prevent Dialysis (FINISHED) program. An additional 284 Indigenous adults who resided in low-income areas in the city of Winnipeg, Manitoba, Canada were screened as part of the NorWest Mobile Diabetes and Kidney Disease Screening and Intervention Project. RESULTS: Our findings indicate that a gradient of CKD and diabetes prevalence exists for Indigenous individuals living in different geographic areas. Compared to urban-dwelling Indigenous individuals, rural-dwelling individuals had more than a 2-fold (2.1, 95% CI = 1.4-3.1) increase in diabetes whereas remote-dwelling individuals had a 4-fold (4.1, 95% CI = 2.8-6.0) increase, and more than a 3-fold (3.1, 95% CI = 2.2-4.5) increase in CKD prevalence. CONCLUSION: Although these results highlight the relative importance of geography in determining the prevalence of diabetes and CKD in Indigenous Canadians, geography is but an important surrogate of other determinants, such as poverty and access to care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.283
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2018
Admission routes2
Has abstractyes

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